Guidelines for Developing a Data Security Policy in the Age of Big Data

When referring to a cloud security policy in the context of a big data use case, we expect any security solution to provide the same flexibility as the cloud without compromising the security of the deployment. But flexibility and security sometimes don't go hand in hand, so how to achieve a balance between security and flexibility is something that cloud providers and big data providers need to think about y.

Deploying cloud encryption measures is considered a first step, but they are not the right solution for all. Some encryption solutions require local gateway encryption, which doesn't work well in a cloud big data environment. Additionally cloud providers offer key encryption, which allows users to enjoy the benefits offered by infrastructure cloud solutions while keeping the keys in their own hands and keeping them in a secure state. To be able to get the best encryption solution for your big data environment, it is recommended to use key encryption.

In the midst of big data, every component of the structure should be able to scale, and cloud security solutions are no exception. When selecting cloud security solutions, users need to ensure that they will work across all cross-regional cloud deployment points. Additionally, they must be able to scale efficiently in a big data infrastructure. Hardware security modules, however, are not suitable for Big Data use cases because they are not scalable and do not have the flexibility to adapt to cloud models. To gain the necessary scalability, it is recommended to use cloud security solutions designed specifically for cloud computing.

In order to automate cloud security policies as much as possible, users should choose a virtualized tool solution over a hardware solution. Users need to understand that available APIs are also part of a cloud security solution. Virtual tools coupled with unused APIs can provide the flexibility and automation needed in cloud big data use cases.

When it comes to big data security, users should categorize data based on its sensitivity and then protect them accordingly. Not all big data infrastructures are secure, and if the data at risk is sensitive or regulated, users may need to look for alternatives.

We are talking about data security, but in fact, big data security also includes the following aspects:

Scale, real-time and distributed processing: the nature of the characteristics of big data (so that big data to address the data management and processing needs of more than the previous data management system, for example, in terms of capacity, real-time, distributed architectures, and parallel processing) makes it more difficult to ensure the security of these systems. difficult. Big data clusters are open and self-organizing, and enable users to communicate with multiple data nodes simultaneously. Verifying which data nodes and which clients should have access to information is difficult. Let's not forget that the intrinsic properties of Big Data mean that new nodes automatically connect to the cluster to *** enjoy the data and query results and solve customer tasks.

Embedded security: In the mad race involving Big Data, most development resources are devoted to improving the scalability, ease of use, and analytics capabilities of Big Data. Very little is devoted to adding security features. However, you want security features that are embedded in the Big Data platform. You want developers to support the required functionality during the design and deployment phases. You want security features that are as scalable, high-performance, and self-organizing as a Big Data cluster. The problem is that open source systems or most commercial systems generally don't include security products. And many security products cannot be embedded in Hadoop or other non-relational databases. Most systems offer minimal security features, but not enough to include all common threats. To a large extent, you need to build your own security policy.

Applications: most applications geared toward big data clusters are Web applications. They utilize Web-based technologies and stateless REST-based APIs.While a full discussion of this aspect of Big Data security is beyond the scope of this article, Web-based applications and APIs pose one of the most significant threats to these Big Data clusters. They can provide unrestricted access to data stored in big data clusters after an attack or compromise. Application security, user access management, and authorization controls are very important and are as essential as security measures focused on securing big data clusters.

In short, only by establishing the most stringent security standards for data can Big Data continue to enjoy the scalability, flexibility, and automation provided by cloud computing.

The above is what I have shared with you about the guidelines for formulating data security strategies in the era of big data, and for more information you can follow Global Green Ivy to share more dry goods